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Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: a Quantitative Comparison with Tracers in the Monkey

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Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: a Quantitative Comparison with Tracers in the Monkey 6758 • The Journal of Neuroscience, June 22, 2016 • 36(25):6758–6770 Systems/Circuits Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey X Chad J. Donahue,1 XStamatios N. Sotiropoulos,2 XSaad Jbabdi,2 XMoises Hernandez-Fernandez,2 Timothy E. Behrens,2 XTim B. Dyrby,3,4 XTimothy Coalson,1 Henry Kennedy,5 XKenneth Knoblauch,5 David C. Van Essen,1* and Matthew F. Glasser1* 1Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri 63110, 2Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom OX3 9DU, 3Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Hvidovre, Denmark 2650, 4Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark 2800, and 5Stem-cell and Brain Research Institute, Bron, France 69500 Tractography based on diffusion MRI offers the promise of characterizing many aspects of long-distance connectivity in the brain, but requires quantitative validation to assess its strengths and limitations. Here, we evaluate tractography’s ability to estimate the presence and strength of connections between areas of macaque neocortex by comparing its results with published data from retrograde tracer injections. Probabilistic tractography was performed on high-quality postmortem diffusion imaging scans from two Old World monkey brains. Tractography connection weights were estimated using a fractional scaling method based on normalized streamline density. We foundacorrelationbetweenlog-transformedtractographyandtracerconnectionweightsofrϭ0.59,twicethatreportedinarecentstudy on the macaque. Using a novel method to estimate interareal connection lengths from tractography streamlines, we regressed out the distance dependence of connection strength and found that the correlation between tractography and tracers remains positive, albeit substantially reduced. Altogether, these observations provide a valuable, data-driven perspective on both the strengths and limitations of tractography for analyzing interareal corticocortical connectivity in nonhuman primates and a framework for assessing future tractog- raphy methodological refinements objectively. Key words: cerebral cortex; connectivity; diffusion tractography; macaque; neuroanatomy; retrograde tracing Significance Statement Tractography based on diffusion MRI has great potential for a variety of applications, including estimation of comprehensive maps of neural connections in the brain (“connectomes”). Here, we describe methods to assess quantitatively tractography’s performance in detecting interareal cortical connections and estimating connection strength by comparing it against published results using neuroanatomical tracers. We found the correlation of tractography’s estimated connection strengths versus tracer to be twice that of a previous study. Using a novel method for calculating interareal cortical distances, we show that tractography- based estimates of connection strength have useful predictive power beyond just interareal separation. By freely sharing these methods and datasets, we provide a valuable resource for future studies in cortical connectomics. Introduction anatomy and circuitry. These include characterizing trajectories Tractography based on diffusion MRI (dMRI) is used widely to of major white matter (WM) fiber bundles (Catani and Thiebaut obtain several complementary types of information about brain de Schotten, 2008; Glasser and Rilling, 2008) and subdividing (parcellating) gray matter (GM) regions based on tractography- Received Feb. 8, 2016; revised May 10, 2016; accepted May 14, 2016. Author contributions: C.J.D., D.C.V.E., and M.F.G. designed research; C.J.D., T.E.B., T.C., H.K., K.K., D.C.V.E., and theFrenchNationalResearchAgency(GrantsANR-11-BSV4-501,ANR-14-CE13-0033,LabExCORTEXANR-11-LABX- M.F.G. performed research; S.N.S., S.J., M.H.-F., T.E.B., T.E.B.D., H.K., T.C., and M.F.G. contributed unpublished 0042, and Universite´ de Lyon ANR-11-IDEX-0007 to H.K.), and the National Institutes of Health (Grant reagents/analytictools;C.J.D.,S.N.S.,S.J.,D.C.V.E.,andM.F.G.analyzeddata;C.J.D.,S.N.S.,S.J.,T.E.B.,T.E.B.D.,H.K., P01AG026423) and the Yerkes National Primate Research Center (Office of Research Infrastructure Programs Grant K.K., T.C., D.C.V.E., and M.F.G. wrote the paper. OD P51OD11132) for the scans used for the macaque atlas, and to T. Preuss and J. Rilling. We thank Drs. R. Palmour This work was supported by the National Institutes of Health (Grant R01 MH 60974 to D.C.V.E. and Grant F30 and M. Ptito and the Behavioral Science Foundation of St-Kitts (West Indies) for providing the Vervet monkey MH097312 to M.F.G.), the Engineering and Physical Sciences Research Council (Grant EP/L023067/1 to S.N.S.), and specimens (PM2) for ex vivo imaging. Donahue et al. • Connection Strength and Distance with Tractography J. Neurosci., June 22, 2016 • 36(25):6758–6770 • 6759 derived connectivity profiles (“connectional contrast”; Behrens two areas relative to the number of streamlines extrinsic to those et al., 2003; Johansen-Berg et al., 2004; Rushworth et al., 2006; areas. To investigate a known tractography-path-length depen- Beckmann et al., 2009; Mars et al., 2011). Here, we focus on using dency (Basser et al., 2000; Liptrot et al., 2014), we compared two tractography to estimate the presence and weight (“strength”) of tractography seeding strategies for their impact on overall trac- long-distance connections between GM regions. This involves tography performance. analysis of “parcellated connectomes”; that is, estimating con- Tracer-based connection weights decline approximately ex- nectivity between brain subdivisions (parcels) in humans or non- ponentially with interareal separation (Ercsey-Ravasz et al., human primates (NHPs) (Sporns et al., 2005; Harriger et al., 2013). Using a new tractography-based method for estimating 2012; Li et al., 2013; Reid et al., 2016; van den Heuvel et al., 2015). interareal separation, we show that tractography remains mod- Tractography is an indirect method for inferring connectivity estly informative in predicting connection presence and weight and various methodological confounds introduce noise and/or after regressing out an exponential relationship with path length. bias (Jbabdi and Johansen-Berg, 2011; Jones et al., 2013; Van These limitations of tractography likely reflect major anatomical Essen et al., 2014). Validation studies are needed that compare features, such as the organization of WM bundles subjacent to against “ground truth” data from anatomical tracer studies in sulcal fundi (Reveley et al., 2015; see Discussion). laboratory animals. Previous studies in NHPs demonstrate both successes and limitations of tractography for assessing pathway Materials and Methods trajectories (Jbabdi et al., 2015; Kno¨sche et al., 2015) and detect- ing the presence of long-distance interareal connections (Jbabdi Macaque retrograde tracer data. Markov et al. (2014) quantified intra- hemispheric interareal connectivity in the macaque cortex using retro- et al., 2013; Thomas et al., 2014; Azadbakht et al., 2015; Reveley et grade tracers and reported weighted connectivity of 29 input injection al., 2015). It is equally important to examine the accuracy of areas in an atlas of 91 cortical areas; that is, a 29 ϫ 91 weighted and tractography-estimated connection weights given the high directed connectivity matrix. This 91-area parcellation, originally gener- density of the cortical graph (Markov et al., 2014) and the fact ated from a histologically based surface reconstruction of an individual that connection weights are fundamental to understanding cor- macaque left hemisphere (M132), was registered to the macaque “F99” tical organization (Ercsey-Ravasz et al., 2013). A recent system- atlas using a landmark-constrained registration algorithm in Caret atic comparison in the mouse (Calabrese et al., 2015) revealed a (Markov et al., 2014). We used the MSM-Sulc algorithm (Robinson et al., correlation coefficient of r ϭ 0.46 between log-transformed, 2014) to register the F99 atlas to a new population average macaque high-resolution postmortem tractography data and quantitative Macaca mulatta atlas (“Yerkes19,” as described below). Figure 1A shows tracer-based connectivity data (Oh et al., 2014). In contrast, van the M132 parcellation displayed on the inflated macaque Yerkes19 atlas ϭ left hemisphere. The locations of cortical area relative to gyral and sulcal den Heuvel et al. (2015) reported much lower correlations (r landmarks on the atlas surface are similar, but not identical, to those in 0.25–0.31) when comparing in vivo macaque tractography with the original M132 surface reconstructed from histological sections. Fig- two published tracer-based connectivity analyses. Given method- ure 1B shows the locations of reported injection sites (Markov et al., ological limitations in data acquisition (e.g., coarse spatial reso- 2014) mapped to the atlas surface. lution, low angular resolution diffusion scans) and analysis (e.g., The weight of the projection from each injected area to any given a coarse cortical parcellation; see Discussion) in the latter study, source area was defined as the FLNe, the fraction of labeled neurons in a this may not reflect the upper bound for tractography perfor- source area relative to the total number of labeled
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